tests/testthat/test-centering.R
In jepusto/scdhlm: Estimating Hierarchical Linear Models for Single-Case Designs
skip_if_not_installed("nlme")
library(nlme)
test_that("calc_BCSMD() effect size does not depend on centering time for Alber-Morgan data.", {
data(AlberMorgan)
cent <- 2
dat <- preprocess_SCD(design = "MBP",
case = case,
phase = condition,
session = session,
outcome = outcome,
center = cent,
data = AlberMorgan)
# Fit the model
fit_RML <- lme(fixed = outcome ~ 1 + I(session^1) + trt + I(session_trt^1),
random = ~ 1 + I(session^1) | case,
correlation = corAR1(0.01, ~ session | case),
data = dat,
control = lmeControl(msMaxIter = 50, apVar = FALSE, returnObject = TRUE))
# Calculate effect size with g_mlm()
A <- 5
B <- 31
p_const <- c(rep(0L, length(c(0,1))), (B - A)^as.integer(c(0,1)))
r_const <- c(c(1, 2 * (B - cent), (B - cent)^2), c(),
c(0), c(), 1L) # specify whether using random effects, cor struct, var struct, and level-1 errors
ES1 <- g_mlm(fit_RML, p_const = p_const, r_const = r_const, infotype = "expected")
ES2 <- calc_BCSMD(design = "MBP",
case = case,
phase = condition,
session = session,
outcome = outcome,
center = cent,
data = AlberMorgan,
FE_base = c(0,1),
RE_base = c(0,1),
FE_trt = c(0,1),
RE_trt = NULL,
A = 5, B = 31,
summary = FALSE)
expect_equal(ES1$g_AB, ES2$g_AB, tol = 1e-5)
expect_equal(ES1$SE_g_AB, ES2$SE_g_AB, tol = 1e-5)
ES3 <- calc_BCSMD(design = "MBP",
case = case,
phase = condition,
session = session,
outcome = outcome,
center = 20,
data = AlberMorgan,
FE_base = c(0,1),
RE_base = c(0,1),
FE_trt = c(0,1),
RE_trt = NULL,
A = 5, B = 31,
summary = FALSE)
expect_equal(ES2$g_AB, ES3$g_AB, tol = 1e-5)
expect_equal(ES2$SE_g_AB, ES3$SE_g_AB, tol = 1e-5)
})
test_that("calc_BCSMD() effect size does not depend on centering time for Peltier data.", {
data(Peltier)
cent <- 13
dat <- preprocess_SCD(design = "MBP",
case = case,
phase = condition,
session = session,
outcome = outcome,
center = cent,
data = Peltier)
# Fit the model
suppressWarnings(
fit_RML <- lme(fixed = outcome ~ 1 + I(session^1) + trt + I(session_trt^1) + I(session_trt^2),
random = ~ 1 + I(session^1) | case,
data = dat,
control = lmeControl(msMaxIter = 50, apVar = FALSE, returnObject = TRUE))
)
# Calculate effect size with g_mlm()
A <- 10
B <- 16
p_const <- c(rep(0L, length(c(0,1))), (B - A)^as.integer(c(0,1,2)))
r_const <- c(c(1, 2 * (B - cent), (B - cent)^2), c(),
c(), c(), 1L) # specify whether using random effects, cor struct, var struct, and level-1 errors
ES1 <- g_mlm(fit_RML, p_const = p_const, r_const = r_const, infotype = "expected")
ES2 <- calc_BCSMD(design = "MBP",
case = case,
phase = condition,
session = session,
outcome = outcome,
center = cent,
data = Peltier,
FE_base = c(0,1),
RE_base = c(0,1),
FE_trt = c(0,1,2),
corStruct = "IID",
A = A, B = B,
summary = FALSE)
expect_equal(ES1$g_AB, ES2$g_AB, tol = 1e-5)
expect_equal(ES1$SE_g_AB, ES2$SE_g_AB, tol = 1e-5)
ES3 <- calc_BCSMD(design = "MBP",
case = case,
phase = condition,
session = session,
outcome = outcome,
center = 30,
data = Peltier,
FE_base = c(0,1),
RE_base = c(0,1),
FE_trt = c(0,1,2),
corStruct = "IID",
A = A, B = B,
summary = FALSE)
expect_equal(ES2$g_AB, ES3$g_AB, tol = 1e-5)
expect_equal(ES2$SE_g_AB, ES3$SE_g_AB, tol = 1e-5)
})
test_that("calc_BCSMD() effect size does not depend on centering time for Bryant data.", {
data(Bryant2018)
cent <- 13
dat <- preprocess_SCD(design = "CMB",
case = case,
phase = treatment,
session = session,
outcome = outcome,
cluster = group,
center = cent,
data = Bryant2018)
# Fit the model
suppressWarnings(
fit_RML <- lme(fixed = outcome ~ 1 + I(session^1) + trt + I(session_trt^1),
random = list(group = ~ 1 + I(session^1), case = ~ 1 + I(session^1) + I(session_trt^1)),
correlation = corAR1(0.01, ~ session | group / case),
data = dat,
control = lmeControl(msMaxIter = 50, apVar = FALSE, returnObject = TRUE))
)
# Calculate effect size with g_mlm()
A <- 1
B <- 59
p_const <- c(rep(0L, length(c(0,1))), (B - A)^as.integer(c(0,1)))
r_const <- c(c(1,2 * (B - cent),(B - cent)^2), c(),
c(1,2 * (B - cent),(B - cent)^2), c(0,0,0),
c(0), c(), 1L) # specify whether using random effects, cor struct, var struct, and level-1 errors
ES1 <- g_mlm(fit_RML, p_const = p_const, r_const = r_const, infotype = "expected")
ES2 <- calc_BCSMD(design = "CMB",
case = case,
phase = treatment,
session = session,
outcome = outcome,
cluster = group,
center = cent,
data = Bryant2018,
FE_base = c(0,1),
RE_base = c(0,1),
RE_base_2 = c(0,1),
FE_trt = c(0,1),
RE_trt = c(1),
corStruct = "AR1",
A = A, B = B,
summary = FALSE)
expect_equal(ES1$g_AB, ES2$g_AB, tol = 1e-5)
expect_equal(ES1$SE_g_AB, ES2$SE_g_AB, tol = 1e-5)
ES3 <- calc_BCSMD(design = "CMB",
case = case,
phase = treatment,
session = session,
outcome = outcome,
cluster = group,
center = cent,
data = Bryant2018,
FE_base = c(0,1),
RE_base = c(0,1),
RE_base_2 = c(0),
FE_trt = c(0,1),
corStruct = "AR1",
A = A, B = B,
summary = FALSE)
ES4 <- calc_BCSMD(design = "CMB",
case = case,
phase = treatment,
session = session,
outcome = outcome,
cluster = group,
center = 1,
data = Bryant2018,
FE_base = c(0,1),
RE_base = c(0,1),
RE_base_2 = c(0),
FE_trt = c(0,1),
corStruct = "AR1",
A = A, B = B,
summary = FALSE)
expect_equal(ES3$g_AB, ES4$g_AB, tol = 1e-3)
expect_equal(ES3$SE_g_AB, ES4$SE_g_AB, tol = 1e-3)
ES5 <- calc_BCSMD(design = "CMB",
case = case,
phase = treatment,
session = session,
outcome = outcome,
cluster = group,
center = 40,
data = Bryant2018,
FE_base = c(0,1),
RE_base = c(0,1),
RE_base_2 = c(0),
FE_trt = c(0,1),
corStruct = "AR1",
A = A, B = B,
summary = FALSE)
expect_equal(ES3$g_AB, ES5$g_AB, tol = 1e-3)
expect_equal(ES3$SE_g_AB, ES5$SE_g_AB, tol = 1e-3)
})
test_that("calc_BCSMD() effect size does not depend on centering time for Thiemann data.", {
data(Thiemann2001)
cent <- 20
dat <- preprocess_SCD(design = "RMBB",
case = case,
series = series,
phase = treatment,
session = time,
outcome = outcome,
center = cent,
data = Thiemann2001)
# Fit the model
suppressWarnings(
fit_RML <- lme(fixed = outcome ~ 1 + I(time^1) + trt + I(time_trt^1) + I(time_trt^2),
random = list(case = ~ 1 + trt, series = ~ 1 + I(time^1)),
correlation = corAR1(0.01, ~ time | case / series),
data = dat,
control = lmeControl(msMaxIter = 50, apVar = FALSE, returnObject = TRUE))
)
# Calculate effect size with g_mlm()
A <- 5
B <- 30
p_const <- c(rep(0L, length(c(0,1))), (B - A)^as.integer(c(0,1,2)))
r_const <- c(c(1), c(0,0),
c(1,2 * (B - cent),(B - cent)^2), c(),
c(0), c(), 1L) # specify whether using random effects, cor struct, var struct, and level-1 errors
ES1 <- g_mlm(fit_RML, p_const = p_const, r_const = r_const, infotype = "expected")
ES2 <- calc_BCSMD(design = "RMBB",
case = case,
series = series,
phase = treatment,
session = time,
outcome = outcome,
center = cent,
data = Thiemann2001,
FE_base = c(0,1),
RE_base = c(0,1),
RE_base_2 = c(0),
FE_trt = c(0,1,2),
RE_trt_2 = c(0),
corStruct = "AR1",
A = A, B = B,
summary = FALSE)
expect_equal(ES1$g_AB, ES2$g_AB, tol = 1e-5)
expect_equal(ES1$SE_g_AB, ES2$SE_g_AB, tol = 1e-5)
ES3 <- calc_BCSMD(design = "RMBB",
case = case,
series = series,
phase = treatment,
session = time,
outcome = outcome,
center = 10,
data = Thiemann2001,
FE_base = c(0,1),
RE_base = c(0,1),
RE_base_2 = c(0),
FE_trt = c(0,1,2),
RE_trt_2 = c(0),
corStruct = "AR1",
A = A, B = B,
summary = FALSE)
expect_equal(ES2$g_AB, ES3$g_AB, tol = 1e-3)
expect_equal(ES2$SE_g_AB, ES3$SE_g_AB, tol = 1e-3)
})
jepusto/scdhlm documentation built on Feb. 27, 2024, 4:45 p.m.
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skip_if_not_installed("nlme")
library(nlme)
test_that("calc_BCSMD() effect size does not depend on centering time for Alber-Morgan data.", {
data(AlberMorgan)
cent <- 2
dat <- preprocess_SCD(design = "MBP",
case = case,
phase = condition,
session = session,
outcome = outcome,
center = cent,
data = AlberMorgan)
# Fit the model
fit_RML <- lme(fixed = outcome ~ 1 + I(session^1) + trt + I(session_trt^1),
random = ~ 1 + I(session^1) | case,
correlation = corAR1(0.01, ~ session | case),
data = dat,
control = lmeControl(msMaxIter = 50, apVar = FALSE, returnObject = TRUE))
# Calculate effect size with g_mlm()
A <- 5
B <- 31
p_const <- c(rep(0L, length(c(0,1))), (B - A)^as.integer(c(0,1)))
r_const <- c(c(1, 2 * (B - cent), (B - cent)^2), c(),
c(0), c(), 1L) # specify whether using random effects, cor struct, var struct, and level-1 errors
ES1 <- g_mlm(fit_RML, p_const = p_const, r_const = r_const, infotype = "expected")
ES2 <- calc_BCSMD(design = "MBP",
case = case,
phase = condition,
session = session,
outcome = outcome,
center = cent,
data = AlberMorgan,
FE_base = c(0,1),
RE_base = c(0,1),
FE_trt = c(0,1),
RE_trt = NULL,
A = 5, B = 31,
summary = FALSE)
expect_equal(ES1$g_AB, ES2$g_AB, tol = 1e-5)
expect_equal(ES1$SE_g_AB, ES2$SE_g_AB, tol = 1e-5)
ES3 <- calc_BCSMD(design = "MBP",
case = case,
phase = condition,
session = session,
outcome = outcome,
center = 20,
data = AlberMorgan,
FE_base = c(0,1),
RE_base = c(0,1),
FE_trt = c(0,1),
RE_trt = NULL,
A = 5, B = 31,
summary = FALSE)
expect_equal(ES2$g_AB, ES3$g_AB, tol = 1e-5)
expect_equal(ES2$SE_g_AB, ES3$SE_g_AB, tol = 1e-5)
})
test_that("calc_BCSMD() effect size does not depend on centering time for Peltier data.", {
data(Peltier)
cent <- 13
dat <- preprocess_SCD(design = "MBP",
case = case,
phase = condition,
session = session,
outcome = outcome,
center = cent,
data = Peltier)
# Fit the model
suppressWarnings(
fit_RML <- lme(fixed = outcome ~ 1 + I(session^1) + trt + I(session_trt^1) + I(session_trt^2),
random = ~ 1 + I(session^1) | case,
data = dat,
control = lmeControl(msMaxIter = 50, apVar = FALSE, returnObject = TRUE))
)
# Calculate effect size with g_mlm()
A <- 10
B <- 16
p_const <- c(rep(0L, length(c(0,1))), (B - A)^as.integer(c(0,1,2)))
r_const <- c(c(1, 2 * (B - cent), (B - cent)^2), c(),
c(), c(), 1L) # specify whether using random effects, cor struct, var struct, and level-1 errors
ES1 <- g_mlm(fit_RML, p_const = p_const, r_const = r_const, infotype = "expected")
ES2 <- calc_BCSMD(design = "MBP",
case = case,
phase = condition,
session = session,
outcome = outcome,
center = cent,
data = Peltier,
FE_base = c(0,1),
RE_base = c(0,1),
FE_trt = c(0,1,2),
corStruct = "IID",
A = A, B = B,
summary = FALSE)
expect_equal(ES1$g_AB, ES2$g_AB, tol = 1e-5)
expect_equal(ES1$SE_g_AB, ES2$SE_g_AB, tol = 1e-5)
ES3 <- calc_BCSMD(design = "MBP",
case = case,
phase = condition,
session = session,
outcome = outcome,
center = 30,
data = Peltier,
FE_base = c(0,1),
RE_base = c(0,1),
FE_trt = c(0,1,2),
corStruct = "IID",
A = A, B = B,
summary = FALSE)
expect_equal(ES2$g_AB, ES3$g_AB, tol = 1e-5)
expect_equal(ES2$SE_g_AB, ES3$SE_g_AB, tol = 1e-5)
})
test_that("calc_BCSMD() effect size does not depend on centering time for Bryant data.", {
data(Bryant2018)
cent <- 13
dat <- preprocess_SCD(design = "CMB",
case = case,
phase = treatment,
session = session,
outcome = outcome,
cluster = group,
center = cent,
data = Bryant2018)
# Fit the model
suppressWarnings(
fit_RML <- lme(fixed = outcome ~ 1 + I(session^1) + trt + I(session_trt^1),
random = list(group = ~ 1 + I(session^1), case = ~ 1 + I(session^1) + I(session_trt^1)),
correlation = corAR1(0.01, ~ session | group / case),
data = dat,
control = lmeControl(msMaxIter = 50, apVar = FALSE, returnObject = TRUE))
)
# Calculate effect size with g_mlm()
A <- 1
B <- 59
p_const <- c(rep(0L, length(c(0,1))), (B - A)^as.integer(c(0,1)))
r_const <- c(c(1,2 * (B - cent),(B - cent)^2), c(),
c(1,2 * (B - cent),(B - cent)^2), c(0,0,0),
c(0), c(), 1L) # specify whether using random effects, cor struct, var struct, and level-1 errors
ES1 <- g_mlm(fit_RML, p_const = p_const, r_const = r_const, infotype = "expected")
ES2 <- calc_BCSMD(design = "CMB",
case = case,
phase = treatment,
session = session,
outcome = outcome,
cluster = group,
center = cent,
data = Bryant2018,
FE_base = c(0,1),
RE_base = c(0,1),
RE_base_2 = c(0,1),
FE_trt = c(0,1),
RE_trt = c(1),
corStruct = "AR1",
A = A, B = B,
summary = FALSE)
expect_equal(ES1$g_AB, ES2$g_AB, tol = 1e-5)
expect_equal(ES1$SE_g_AB, ES2$SE_g_AB, tol = 1e-5)
ES3 <- calc_BCSMD(design = "CMB",
case = case,
phase = treatment,
session = session,
outcome = outcome,
cluster = group,
center = cent,
data = Bryant2018,
FE_base = c(0,1),
RE_base = c(0,1),
RE_base_2 = c(0),
FE_trt = c(0,1),
corStruct = "AR1",
A = A, B = B,
summary = FALSE)
ES4 <- calc_BCSMD(design = "CMB",
case = case,
phase = treatment,
session = session,
outcome = outcome,
cluster = group,
center = 1,
data = Bryant2018,
FE_base = c(0,1),
RE_base = c(0,1),
RE_base_2 = c(0),
FE_trt = c(0,1),
corStruct = "AR1",
A = A, B = B,
summary = FALSE)
expect_equal(ES3$g_AB, ES4$g_AB, tol = 1e-3)
expect_equal(ES3$SE_g_AB, ES4$SE_g_AB, tol = 1e-3)
ES5 <- calc_BCSMD(design = "CMB",
case = case,
phase = treatment,
session = session,
outcome = outcome,
cluster = group,
center = 40,
data = Bryant2018,
FE_base = c(0,1),
RE_base = c(0,1),
RE_base_2 = c(0),
FE_trt = c(0,1),
corStruct = "AR1",
A = A, B = B,
summary = FALSE)
expect_equal(ES3$g_AB, ES5$g_AB, tol = 1e-3)
expect_equal(ES3$SE_g_AB, ES5$SE_g_AB, tol = 1e-3)
})
test_that("calc_BCSMD() effect size does not depend on centering time for Thiemann data.", {
data(Thiemann2001)
cent <- 20
dat <- preprocess_SCD(design = "RMBB",
case = case,
series = series,
phase = treatment,
session = time,
outcome = outcome,
center = cent,
data = Thiemann2001)
# Fit the model
suppressWarnings(
fit_RML <- lme(fixed = outcome ~ 1 + I(time^1) + trt + I(time_trt^1) + I(time_trt^2),
random = list(case = ~ 1 + trt, series = ~ 1 + I(time^1)),
correlation = corAR1(0.01, ~ time | case / series),
data = dat,
control = lmeControl(msMaxIter = 50, apVar = FALSE, returnObject = TRUE))
)
# Calculate effect size with g_mlm()
A <- 5
B <- 30
p_const <- c(rep(0L, length(c(0,1))), (B - A)^as.integer(c(0,1,2)))
r_const <- c(c(1), c(0,0),
c(1,2 * (B - cent),(B - cent)^2), c(),
c(0), c(), 1L) # specify whether using random effects, cor struct, var struct, and level-1 errors
ES1 <- g_mlm(fit_RML, p_const = p_const, r_const = r_const, infotype = "expected")
ES2 <- calc_BCSMD(design = "RMBB",
case = case,
series = series,
phase = treatment,
session = time,
outcome = outcome,
center = cent,
data = Thiemann2001,
FE_base = c(0,1),
RE_base = c(0,1),
RE_base_2 = c(0),
FE_trt = c(0,1,2),
RE_trt_2 = c(0),
corStruct = "AR1",
A = A, B = B,
summary = FALSE)
expect_equal(ES1$g_AB, ES2$g_AB, tol = 1e-5)
expect_equal(ES1$SE_g_AB, ES2$SE_g_AB, tol = 1e-5)
ES3 <- calc_BCSMD(design = "RMBB",
case = case,
series = series,
phase = treatment,
session = time,
outcome = outcome,
center = 10,
data = Thiemann2001,
FE_base = c(0,1),
RE_base = c(0,1),
RE_base_2 = c(0),
FE_trt = c(0,1,2),
RE_trt_2 = c(0),
corStruct = "AR1",
A = A, B = B,
summary = FALSE)
expect_equal(ES2$g_AB, ES3$g_AB, tol = 1e-3)
expect_equal(ES2$SE_g_AB, ES3$SE_g_AB, tol = 1e-3)
})
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